scholarly journals Anisotropic Diffusion Based Multiplicative Speckle Noise Removal

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3164 ◽  
Author(s):  
Mei Gao ◽  
Baosheng Kang ◽  
Xiangchu Feng ◽  
Wei Zhang ◽  
Wenjuan Zhang
Keyword(s):  
Standard Deviation ◽  
Anisotropic Diffusion ◽  
Additive Noise ◽  
Ground Truth ◽  
Speckle Noise ◽  
Noise Removal ◽  
Noisy Image ◽  
Stopping Criteria ◽  
Proposed Model ◽  
Optimal Values

Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.

Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

2021 ◽  
Vol 263 (2) ◽  
pp. 4441-4445
Author(s):  
Hyunsuk Huh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Background Noise ◽  
Ground Truth ◽  
Noise Removal ◽  
Industrial Manufacturing ◽  
Proposed Model ◽  
Audio Data ◽  
Passive Noise ◽  
Removal Technique ◽  
Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

scholarly journals A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Chen ◽  
Jin-Lin Cai ◽  
Wen-Sheng Chen ◽  
Yan Li
Keyword(s):  
Fourth Order ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Speckle Noise ◽  
Noise Removal ◽  
Equation Model ◽  
Superior Performance ◽  
Pde Model ◽  
Novel Approach ◽  
Fourth Order Pde

Multiplicative noise, also known as speckle noise, is signal dependent and difficult to remove. Based on a fourth-order PDE model, this paper proposes a novel approach to remove the multiplicative noise on images. In practice, Fourier transform and logarithm strategy are utilized on the noisy image to convert the convolutional noise into additive noise, so that the noise can be removed by using the traditional additive noise removal algorithm in frequency domain. For noise removal, a new fourth-order PDE model is developed, which avoids the blocky effects produced by second-order PDE model and attains better edge-preserve ability. The performance of the proposed method has been evaluated on the images with both additive and multiplicative noise. Compared with some traditional methods, experimental results show that the proposed method obtains superior performance on different PSNR values and visual quality.

A Statistical Salt-and-Pepper Noise Removal Algorithm

2019 ◽  
Vol 19 (01) ◽  
pp. 1950006 ◽  
Author(s):  
Amiya Halder ◽  
Sayan Halder ◽  
Samrat Chakraborty ◽  
Apurba Sarkar
Keyword(s):  
Standard Deviation ◽  
Noise Removal ◽  
Noisy Image ◽  
Salt And Pepper Noise ◽  
Novel Approach ◽  
Noise Density ◽  
Salt And Pepper

This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.

Automatic Anisotropic Diffusion Filtering and Graph-search Segmentation of Macular Spectral-domain Optical Coherence Tomographic (SD-OCT) Images

2019 ◽  
Vol 15 (3) ◽  
pp. 308-318 ◽  
Author(s):  
A. Usha ◽  
Nijisha Shajil ◽  
M. Sasikala
Keyword(s):  
Anisotropic Diffusion ◽  
Ground Truth ◽  
Speckle Noise ◽  
Graph Search ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Dijkstra Algorithm ◽  
Image Processing Algorithm ◽  
Cross Sectional ◽  
Sd Oct

Background: Optical Coherence Tomography (OCT) is a non-invasive medical imaging technique that provides high-resolution cross-sectional images of the retina. There is a need to develop algorithms for obtaining quantitative and qualitative information about the retina which are essential for assessing and managing eye conditions. Methods: This work emphasizes on an automated image processing algorithm for segmenting retinal layers. It involves preprocessing of the acquired retinal SD-OCT image (B-scan) using the proposed automatic Anisotropic diffusion filter, followed with contrast stretching to suppress intrinsic speckle noise without blurring structural edges. Graph search segmentation using Dijkstra algorithm with a combination of threshold and axial gradient as the cost function is used to segment the retinal layer boundaries. Results: The algorithm was performed and the average thickness of the segmented retina was computed for the 3D retinal scan (128 B-scans) of 8 subjects (4 normal and 4 abnormal) using Early Treatment Diabetic Retinopathy Screening (ETDRS) chart. Conclusion: Segmentation was evaluated using manually segmented B-scan by an Ophthalmologist as ground truth and accuracy was found to be 99.14 ± 0.27%.

scholarly journals Image Structure-Preserving Denoising Based on Difference Curvature Driven Fractional Nonlinear Diffusion

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Xuehui Yin ◽  
Shangbo Zhou
Keyword(s):  
Fractional Order ◽  
Anisotropic Diffusion ◽  
Noise Removal ◽  
Experimental Results ◽  
New Techniques ◽  
Proposed Model ◽  
Staircase Effect ◽  
The Difference ◽  
Curvature Driven ◽  
Difference Curvature

The traditional integer-order partial differential equations and gradient regularization based image denoising techniques often suffer from staircase effect, speckle artifacts, and the loss of image contrast and texture details. To address these issues, in this paper, a difference curvature driven fractional anisotropic diffusion for image noise removal is presented, which uses two new techniques, fractional calculus and difference curvature, to describe the intensity variations in images. The fractional-order derivatives information of an image can deal well with the textures of the image and achieve a good tradeoff between eliminating speckle artifacts and restraining staircase effect. The difference curvature constructed by the second order derivatives along the direction of gradient of an image and perpendicular to the gradient can effectively distinguish between ramps and edges. Fourier transform technique is also proposed to compute the fractional-order derivative. Experimental results demonstrate that the proposed denoising model can avoid speckle artifacts and staircase effect and preserve important features such as curvy edges, straight edges, ramps, corners, and textures. They are obviously superior to those of traditional integral based methods. The experimental results also reveal that our proposed model yields a good visual effect and better values of MSSIM and PSNR.

scholarly journals Rician Noise Removal via a Learned Dictionary

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jian Lu ◽  
Jiapeng Tian ◽  
Lixin Shen ◽  
Qingtang Jiang ◽  
Xueying Zeng ◽  
...  
Keyword(s):  
Image Processing ◽  
Noise Removal ◽  
Sparse Representations ◽  
Computational Results ◽  
Rician Noise ◽  
Noisy Image ◽  
Dual Algorithm ◽  
Proposed Model ◽  
Learned Dictionary ◽  
Primal Dual

This paper proposes a new effective model for denoising images with Rician noise. The sparse representations of images have been shown to be efficient approaches for image processing. Inspired by this, we learn a dictionary from the noisy image and then combine the MAP model with it for Rician noise removal. For solving the proposed model, the primal-dual algorithm is applied and its convergence is studied. The computational results show that the proposed method is promising in restoring images with Rician noise.

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